Systems and methods for updating detection models and maintaining data privacy

ABSTRACT

The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.

FIELD

The present application generally relates to systems for evaluating andsharing detection models while maintaining data privacy and methods forusing the same.

BACKGROUND

Analytical models for event detection are important to a range of fieldsand industries. For example, various analytical models are used todetect banking fraud, aid in regulatory compliance, and many othercomplex, data-driven problems. Many fields require the most up-to-datemodels for accurate and timely event detection. In some fields, forexample, many types of fraud, a third party is agent is actively workingto escape detection by current analytical models. Thus, what is neededis a system for updating detection models that allows a model update tobe distributed, analyzed, and implemented in a rapid fashion overmultiple local nodes of the system. Further, because manual creation ofupdated detection models can be a slow and time consuming process, whatis needed is a system that creates its own model updates from successfulevent detection and then distributes the created model update to therest of the system while maintaining data privacy of the location thatcreated the model update.

SUMMARY

Embodiments herein provide a system for updating detection models,comprising: at least one local node comprising: a monitoring module; adiagnosis module; an evaluation module; one or more current detectionmodels; and system data produced by the current detection models; and amemory comprising instructions, which are executed by at least oneprocessor, configured to: receive, by the monitoring module, a modelupdate; determine, by the diagnosis module, the current detectionmodels; determine, by the evaluation module, if the model update shouldbe applied to the current detection models; determine, by the evaluationmodule, if the local node has permission to apply the model update; andupdate, by the evaluation module, the current detection models with themodel update.

In some embodiments, the system further comprises at least a secondlocal node; a central module, wherein the monitoring module of eachlocal node is in electronic communication with the central module; and adatabase of all available models for the system in electroniccommunication with the central module; and a memory comprisinginstructions, which are executed by at least one processor, configuredto: create a model update, comprising: detecting, by the diagnosismodule, a significant change in system data; determining, by thediagnosis module, a list of all current detection models involved withthe detection step; analyzing, by the diagnosis module, the system datainvolved with the detection step; generating, by the diagnosis module,the model update; transmitting, by the monitoring module; the modelupdate distribute a model update, comprising: receiving, by the centralmodule, the model update; analyzing, by the central module, the databaseof available models; determining, by the central module, a prioritylevel for the model update; determining, by the central module, whichlocal nodes should receive the model update; and transmitting, by thecentral module, the model update; and update at least one local node,comprising: receiving, by at least one monitoring module, a modelupdate; determining, by at least one diagnosis module, the currentdetection models; determining, by at least one evaluation module, if themodel update should be applied to the current detection models;determining, by at least one evaluation module, if the local node haspermission to apply the model update; and updating, by at leastevaluation module, the current detection models with the model update.

Embodiments herein also provide a computer implemented method in a dataprocessing system comprising a processor and a memory comprisinginstructions which are executed by the processor to cause the processorto implement a system updating detection models, the method comprising:updating at least one local node, comprising: receiving, by a monitoringmodule of at least one local node, a model update; determining, by adiagnosis module of the local node, the current detection models in useby the local node; determining, by an evaluation module of the localnode, if the model update should be applied to the current detectionmodels; determining, by the evaluation module of the local node, if thelocal node has permission to apply the model update; and updating, bythe evaluation module of the local node, the current detection modelswith the model update.

Embodiments here also provide a computer program product for updatingdetection models, the computer program product comprising at least onecomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a processor to causethe processor to update at least one local node by: receiving, by atleast one monitoring module, the model update; determining, by at leastone diagnosis module, the current detection models; determining, by atleast one evaluation module, if the model update should be applied tothe current detection models; determining, by at least one evaluationmodule, if the local node has permission to apply the model update; andupdating, by at least evaluation module, the current detection modelswith the model update.

Additional features and advantages of this disclosure will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present disclosure are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the disclosure, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that thedisclosure is not limited to the specific embodiments disclosed.

FIG. 1 depicts a block diagram of an exemplary update system comprisinga single local node;

FIG. 2 depicts a block diagram of an exemplary update system comprisingmultiple local nodes connected by central module;

FIG. 3 depicts a block diagram of an exemplary update system comprisingmultiple, directly-connected local nodes;

FIG. 4 depicts a flow chart of an exemplary method of updatinganalytical systems using an update system and a manually created modelupdate;

FIG. 5 depicts a flow chart of an exemplary method of updatinganalytical systems using a model update created by one of the localnodes; wherein the local nodes are connected by a central module;

FIG. 6 depicts a flow chart of an exemplary method of updatinganalytical systems using a model updated created by one of the localnodes, wherein the local nodes are directed connected; and

FIG. 7 depicts a block diagram of an example data processing system inwhich aspect of the illustrative embodiments may be implemented.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of,” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the example provided herein without departing from thespirit and scope of the present disclosure.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a head disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Java™, Smalltalk, C++ orthe like, and conventional procedural programming languages, such as the“C” programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-along softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including LAN or WAN, or the connection may be made toan external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operations steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical functions. In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. IBMWatson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like accuracy at speeds far faster than human beings and on amuch larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypotheses    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice, memorization and recall)    -   Predict and sense with situation awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

Embodiments herein relate to a system for updating analytical modelsacross multiple local nodes. As used herein, an individual “local node”refers to software installed by an end user, such as an individualperson or a corporation. In some embodiments the local node comprisesone computer system. In some embodiments, the local node comprisesmultiple computer systems or servers controlled by the end user. In someembodiments, each local node in the system uses a set of currentanalytical models that are specific to that local node. In someembodiments, each local node in the system accesses and analyzes systemdata produced by one or more analytical models. This system data isspecific to each local node, and may comprise sensitive or confidentialinformation.

As used herein, an individual “analytical model,” or just “model” is asoftware algorithm designed to detect certain events using data analysistechniques. In some embodiments, the analytical models detect dataanomalies. In some embodiments, the analytical models detect fraudevents. In some embodiments, the data analysis techniques used by theanalytical models include, but are not limited to, data preprocessingtechniques, calculation of one or more statistical parameters,statistical ratios based on classifications or groups, calculation ofprobabilities, classification techniques such as data clustering anddata matching, regression analysis, and gap analysis. In someembodiments, the software of the local node comprises one or moreanalytical models. In some embodiments, the software of the local nodecomprises one or more analytical models and deterministic rules. In someembodiments, the software of the local node comprises one or moreanalytical models for fraud detection. In some embodiments, the softwareof the local node comprises one or more analytical models for regulatorycompliance or non-compliance. In some embodiments, the software of thelocal node comprises one or more models and deterministic rules forfraud detection. In some embodiments, the software of the local nodecomprises one or more models and deterministic rules for regulatorycompliance or non-compliance.

In some embodiments, the update system receives one or more modelupdates and pushes those updates to applicable local nodes. In someembodiments, the update system pushes updates to all local nodes in thesystem. In some embodiments, the update system pushes updates to onlyselected local nodes. In some embodiments, the update system determineswhich local nodes receive the model update push.

In some embodiments, each individual local node that receives a modelupdate checks that update against the current models of an analyticalsystem, and, if applicable, the update system will update the currentmodels. In some embodiments, the update system receives one or moremanually created model updates. In some embodiments, the update systemreceives one or more model updates created by a local node of the updatesystem. In some embodiments, the local nodes of the update system areconnected by a central hub or module that itself is not a local node. Insome embodiments, the local modes of the update system are connecteddirectly to each other, for example, as a decentralized network.

In some embodiments, the update system, including any local nodes, is astand-alone system that creates and pushes model updates for anysoftware system that uses analysis models. In some embodiments, theupdate system is itself a component or subsystem of a larger analyticalsystem, for example, an analytical system for fraud detection.

FIG. 1 depicts a block diagram representation of components, outputs anddata flows of an exemplary single local node of an update system 100.The local node comprises three main modules, or subsystems: a monitoringmodule 101, a diagnosis module 102, and an evaluation module 103.

The monitoring module 101 monitors one or more factors to determine if amodel update process is required. In some embodiments, the monitoringmodule 101 checks the time since the last update process and initiatesan update process if enough time has passed. In some embodiments, themonitoring module 101 initiates an update process if 6 hours, 12 hours,1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 10 days, 15 days,30 days, 1 month, 2 months, 3 months, 6 months, or 1 year has passedsince the last update process. In some embodiments, the monitoringmodule 101 initiates an update process if it receives a model updatepushed from a source external to the local node 100. For example, themonitoring module 101 can receive a model update pushed from a centralmodule of the update system, another local node, or directly from anupdate system administrator.

In some embodiments, the monitoring module 101 can initiate an updateprocess if signaled by the diagnosis module 102. In some embodiments,the diagnosis module 102 analyzes system data 104 and can signal themonitoring module 201 to initiate an update process if one or more datathresholds have been met. For example, the diagnosis module 102 cansignal the monitoring module 101 if the diagnosis module's analysis ofthe system data 104 shows an increase in event detection above a datathreshold value or a decrease in event detection below a data thresholdvalue. In some embodiments, the data threshold value can be manuallyset, for example, by an end user. In some embodiments, the datathreshold value can be automatically determined by the diagnosis module102, for example, if the event detection rate increases by a significantvalue over the one week running average detection rate.

When an update process has been initiated, the monitoring module 101will query for available model updates. In some embodiments, themonitoring module 101 will query a central module of the update system,another local mode, or an update system administrator. In someembodiments, if the update process was initiated by a model updatepushed from a source external to the local node 100, then the monitoringmodule 101 will not query for additional available model updates. Insome embodiments, if the update process was initiated by a model updatepushed from a source external to the local node 100, the monitoringmodule 101 will still query for additional available model updates.

When the monitoring module 101 has completed all available queries andhas received at least one model update, the monitoring module 101 willpass the model update to the diagnosis module 102. The diagnosis module102 will compare the model update to a database of current models 105available in the local node. In some embodiments, the diagnosis module102 will categorize the model update to current models 105, whetherthose current models 105 are actively in use or not. In some embodimentsthe diagnosis module 102 will categorize the model update to the systemdata 104 generated by the application of the active current models 105.

When the diagnosis module 102 has received the model update and at leastcompared the model update to the database of current models 105, thediagnosis module 102 will pass the model update and all availablecomparison and other analytical data to the evaluation module 103. Theevaluation module 103 will evaluate the model update to determine if theupdate 106 should be applied. In some embodiments, the evaluation module103 will automatically apply the model update, changing or modify thecurrent models 105 with the model update. In some embodiments, theevaluation module 103 will analyze the model update to determine if sucha model already exists in the current model database 105. In someembodiments, the evaluation module 103 will run the model update againstrelevant system data 104 or relevant categorical data generated by thediagnosis module 102 to determine if the model update will provide thelocal node 100 with different system data than what the current models105 can generate. In some embodiments, the evaluation module 103 willnot automatically apply any updates or perform any analysis unlessauthorized by an end user or administrator of the local node 100.

FIG. 2 depicts a block diagram representation components, outputs anddata flows of an update system with multiple local nodes 200. The updatesystem comprises a central module 201 that connects all local nodes inthe update system 200. FIG. 2 depicts two local nodes, generallycategorized as 210 and 220. In some embodiments, there is no limit tothe number of local nodes that could be present in the update system200. It should be appreciated that local nodes 210 and 220 are generallythe same as the local node described in FIG. 1, with each comprising amonitoring module 211, 221, a diagnosis module 212, 222, and anevaluation module 213, 223. Each local node further comprises its ownsystem data 214, 224 and database of current models 215, 225. It shouldbe appreciated that each local node may have different system data andcurrent models. In some embodiments, the system data 214 may or may notbe identical or similar to system data 224. In some embodiments, thecurrent models 215 may or may not be identical or similar to the currentmodels 225.

The central module 201 does not exist in any local node, but rather in aseparate location, such as a centralized administration server. In someembodiments, the central module 201 can send and receive informationfrom monitoring modules 211, 221. In some embodiments, the centralmodule 201 can send and receive information from any monitoring modulein the update system. The central module 201 can access a masterdatabase of available models 202 to the update system. The database ofavailable models 202 is a listing of all possible analytical models thatcurrently exist in the update system. In some embodiments, a database ofcurrent models in an individual node, for example the current models215, is equivalent to the dataset of available models 202. In someembodiments, a database of current models in an individual node, forexample the current models 215, is not equivalent to the dataset ofavailable models 202, but contains at least one model in common with thedatabase of available models 202.

In some embodiments, when a monitoring module in an individual node, forexample the monitoring module 211, initiates a query for available modelupdates, the monitoring module will electronically communicate with thecentral module 201.

In some embodiments, each individual node can communicate with one ormore end users. In FIG. 2 for example, the evaluation module 213 of node210 can communicate with end user 217. In some embodiments, any moduleof an individual node can communicate with an end user. In someembodiments, an individual node communicates with an end user to providethe end user with information regarding the update process. In someembodiments, an individual node communicates with an end user to providethe end user with information regarding the results of an update, forexample, which models were updated. In some embodiments, an individualnode communicates with an end user to ask the end user for authorizationprior to updating any models.

FIG. 3 depicts another block diagram representation of components,outputs and data flows of an update system with multiple local nodes300. The update system 300 depicts two local nodes, generallycategorized as 310 and 320. In some embodiments, there is no limit tothe number of local nodes that could be present in the update system300. It should be appreciated that local nodes 310 and 320 are generallythe same as the local nodes described in FIGS. 1 and 2, with eachcomprising a monitoring module 311, 321, a diagnosis module 312, 322,and an evaluation module 313, 323. Each local node further comprises itsown system data 314, 324 and database of current models 315, 325. Itshould be appreciated that each local node may have different systemdata and current models. In some embodiments, the system data 314 may ormay not be identical or similar to system data 324. In some embodiments,the current models 315 may or may not be identical or similar to thecurrent models 325.

Unlike FIG. 2, the update system 300 does not have any type of centralmodule that connects all of the local nodes. Instead, each local node isdirectly connected to each other via a network. In some embodiments,each monitoring module is in electronic communication with every othermonitoring module in the update system 300. For example, as depicted inFIG. 3, the monitoring module 311 is in electronic communication withmonitoring module 321.

In some embodiments, when an update process has been initiated in anindividual node, the monitoring module of that node will query anotherlocal node in the update system 300. For example, when an update processhas been initiated in local node 310, the monitoring module 311 willquery monitoring module 321 of local node 320. In some embodiments, whenan update process has been initiated in an individual node, themonitoring module of that node will query all other local nodes in theupdate system 300. In some embodiments, when an update process has beeninitiated in an individual node, the monitoring module of that node willquery only selected other nodes in the update system 300. In someembodiments, when an update process has been initiated in an individualnode, the monitoring module of that node will query only one other nodein the update system 300.

In any embodiment herein, a system administrator can create an updatedmodel and manually add it to the update system. For example, a systemadministrator can create an updated model and submit that model to thecentral module 201 as depicted in FIG. 2. As another example, a systemadministrator can create an updated model and submit that model to themonitoring module 321 as depicted in FIG. 3. In some embodiments, whenan updated model has been added to any update system depicted herein,update process may be initiated throughout all of some of the nodes inthe update system.

In any embodiment herein, any local node of an update system canoriginate a model update and automatically push it to the rest of theupdate system. In some embodiments, local nodes generating their ownmodel updates is advantageous because it allows the update system toquickly respond to increases in fraud detection without end user oradministrator involvement. For example, the diagnosis module 212 asdepicted in FIG. 2 analyzes system data 214 and detects an increase infraud detection greater than a pre-set threshold. The diagnosis module212 proceeds to list the model or models that were used to detect theincrease in fraud, and analyze the system data 214 to determine thecritical features and conditions of the nexus between the model ormodels and the data. The diagnosis module 212 then strips the model ofany specific data to the system data 214 and the local node 210. Themonitoring module 211 then sends the model to the central module 201,which would then determine if the model is applicable as a model updatefor the update system 200.

In any embodiment where a local node is originating a model update forthe update system, it is important that the specific system data of thatlocal node is not shared with any central hub or other local node in theupdate system. In some embodiments, the diagnosis module creating themodel to be shared with the update system creates a new model that isindependent of any specific system data from the local node. In someembodiments, the new model comprises one or more of the following: oneor more algorithms, create date and time, number of events detected overgiven time period, metadata or high level aggregate statistics such astotal transactional value of time, and the threshold point or pointsused to trigger the update. In some embodiments, the new model comprisesratio statistics of one or more data group averages. In someembodiments, the new model can detect deviation from the ratiostatistics of one or more data group averages to determine futurepositive results. In some embodiments, the new model comprises one ormore network or image graphics that represent one or more models. Insome embodiments, the new model comprises one or more network or imagegraphics that represent the new model.

In any of the embodiment herein, the components of the update system canbe stored in the same location, for example, as installed software in aninternal server system at a company, such as a bank. In someembodiments, some of the components of any update system disclosedherein are stored in different locations, such as part of a cloud-basedservice.

FIG. 4 depicts a flow chart of an exemplary method of using a manuallycreated model to push a model update in an update system with a centralmodule 400. In some embodiments, method 400 can be used with the updatesystem depicted in FIG. 2. First, a system administrator manuallycreated a new model that will be used to update the system 401. In someembodiments, the new model comprises a new or updated algorithm oralgorithms. In some embodiments, the new model comprises information onwhat criteria is necessary for the new model's use, for example, thetype of business, the amount of system data required, or type ofdetection performed by the model. In some embodiments, the new modelcomprises priority information on how critical the model is to theupdate system. For example, a new model that must be pushed out to alllocal nodes would be given the highest possible priority. In someembodiments, priority information is categorized as either low priority,medium priority, or high priority.

Next, the new model created by the system administrator is pushed to theupdate system, which receives the model 402. In some embodiments, acentral module of an update system receives the model. Upon receivingthe model 402, the central module then updates the model database 403.For example, the central module 201 would update the available modelsdatabase 202 in update system 200 depicted in FIG. 2.

The update system would then determine the applicable end users for thenew model 404. In some embodiments, the central module is determiningwhich end users are applicable. In some embodiments, the central moduledetermines which end users are applicable for the model update bycomparing criteria information in the new model with information on eachend user in addition to the priority information of the new model. Forexample, if the model update for credit card fraud detection has amedium priority, the central module will identify which local nodes inthe update system are involved with credit card fraud detection and thenpush out the model 405 to those identified local nodes. The model updatewould not be pushed out to any remaining local nodes, however, when eachof those remaining local nodes initiates an update process, for example,if enough time has gone by without an update to trigger the monitoringmodule, that local node may then receive the update. In another example,if the model update for credit card fraud detection has a high priority,the central module will output the model 405 to all local nodes. Inanother example, if the model update for credit card fraud detection hasa low priority, the central module will not push out the model to anylocal node right away, and instead wait for each local node to initiatean update process on its own.

Once the model update has been sent out from the central module, it isreceived 411 by at least one local node. In some embodiments, the modelupdate is received by multiple local nodes simultaneously. In someembodiments, the model update is received by the monitoring module inany of the embodiments described herein.

In some embodiments, once a local node has received a model update 411,it is not installed automatically. First, the local node will consultthe current model database to see if the model update will replace anyexisting models 412. Then the local node will determine the relevance ofthe model update to the node 413. For example, in local node 210 ofupdate system 200 depicted in FIG. 2, the model update is received bythe monitoring module 211, and then passed along to the diagnosis module212. The diagnosis module 212 first consults the current model database215 and then determines the relevance of the model update to local node210. In some embodiments, the diagnosis module 212 will end the updateprocess after the determine relevance step 413. In some embodiments, thediagnosis module 212 will end the update process after the determinerelevance step 413 if the diagnosis module 212 determines that the modelupdate is not needed for the local node. In some embodiments, thediagnosis module 212 will end the update process after the determinerelevance step 413 if the diagnosis module 212 determines that the modelupdate is already present in the local node. In some embodiments, thediagnosis module 212 will automatically bypass the determine relevancestep 413 if the model update carries a high priority.

In some embodiments, once the local node has determined that the modelupdate would be relevant or necessary, the local node will determine ifit has permission to apply the model update 414. In some embodiments,the evaluation module of the local node determines if the local node haspermission to apply the model update. In some embodiments, a local nodewill not have permission to install the model update. In someembodiments, a local node will not have automatic permission to installany model update. In some embodiments, a local node must consult or askpermission from an end user prior to installing the model update 416.For example, once the diagnosis module 212 has either determined thatthe model update is relevant or that the model update has a high enoughpriority to bypass the determine relevance step 413, the model update ispassed along to the evaluation module 213. The evaluation module 213then checks the update permission settings of the local node. In someembodiments, if the evaluation module 213 determines that it does nothave permission to install the model update, the evaluation module 213will end the update process. In some embodiments, the evaluation module213 will consult an end user, for example, by issuing a user prompt orby sending an e-mail or other communication to the end user, beforeinstalling the model update.

The local node will install the model update once the local nodedetermines that it has permission to do so 415. In some embodiments, anevaluation module installs the model update. In some embodiments, anymodule of the update system installs the model update. In someembodiments, the model update installs one or more new models to acurrent model database in the local node. In some embodiments, the modelupdate replaces one or more models in a current model database in thelocal node. For example, after permission has been established, theevaluation module 213 updates the current model database 215 with themodel update.

In some embodiments, once the update 415 is complete, the local nodecreates an output report 417. In some embodiments, the output report isshared with an end user. In some embodiments, the output report isshared with a central module of an update system. In some embodiments,the output report contains information on the model update, including,for example, the type of model updated, whether or not any old modelswere replaced, the date and time of the update, whether the new model iscurrently active, or any combination thereof.

FIG. 5 depicts a flow chart of an exemplary method of pushing a modelupdate in an update system with a central module, where the model updatewas created automatically from a local node in the update system 500. Insome embodiments, method 500 can be used with the update system depictedin FIG. 2. First, a local node in an update system will detect a changein the results from their existing models 501. In some embodiments, alocal node in an update system will detect a change in fraud detectionrates. In some embodiments, the change in fraud detection rates is anincrease in fraud detection greater than a pre-set threshold. In someembodiments, the change in fraud detection rates is a significantincrease or decrease in fraud over a given period of time. In someembodiments, a local node in an update system will detect a change indetected fraud magnitude. In some embodiments, the change in fraudmagnitude is an increase in the value or dollar amount of a detectedfraud event greater than a pre-set threshold. In some embodiments, thechange in fraud magnitude is a significant increase in the value ordollar amount of a detected fraud event compared to a running average ormean of detected events. For example, the diagnosis module 212 asdepicted in FIG. 2 analyzes system data 214 and detects an increase inthe fraud detection rate that is greater than a standard deviation awayfrom the 3-month running average fraud detection rate.

Once a change in the results from their existing models has beendetected 501, the local node will list all of the models involved inthat detection 502. In some embodiments, the local node will list all ofthe models directly involved with producing the events detected in step501. In some embodiments, the local node will list all of the modelsdirectly and indirectly involved with producing the events detected instep 501. In some embodiments, the local node will list all activelyrunning models when the events were detected in step 501. For example,the diagnosis module 212, with access to both the system data 214 andthe current model database 215, will list all of the algorithmic modelsthat were directly and indirectly involved with producing the fraudevents that were previously detected in step 501.

Once a local node has listed the models 502 relevant to the detectedchange 501, the local node will analyze the data involved in producingthe events that lead to the detected change 503. In some embodiments,the local node analyzes the system data to determine the features andconditions relevant to the models listed in step 502 in producing theevents that were detected in step 501. In some embodiments, the localnode analysis can include, but is not limited to, ordinary leastsquares, penalized regressions, generalized additive models, quantileregressions, logistical regressions, and gated linear models. In someembodiments, the local node analysis will be transformed variants of therelevant model or models that reduce the complexity of those models. Forexample, placing monotonicity constraints on a non-linear, non-monotonicmodel to orient the model around variable relationship known to be true,or the utilization of monotonic neural networks for machine learningapplications. In some embodiments, the relevant visualizations will berelated but less complex models that approximate the applicable model ormodels, especially machine learning models. For example, surrogatemodels, local interpretable model-agnostic explanations (LIME), maximumactivation analysis, linear regression, and sensitivity analysis.

Once a local node has listed the models 502 and analyzed the relevantdata 503, the local node can then generate the features of the modelupdate 504 that will be sent to the rest of the update system. In someembodiments, a diagnostic module of a local node generates the modelfeatures 504. In some embodiments, the features of the model update arelocal node agnostic, i.e., the model update is usable by any of thelocal nodes in the update system. Therefore, the model update generatedby the local node is stripped of any specific data of that local node.In some embodiments, the model update features comprise one or more ofthe following: one or more algorithms, creation date and time, number ofevents detected over given time period, metadata or high level aggregatestatistics such as total transactional value of time, and the thresholdpoint or points used to trigger the update. In some embodiments, themodel update features comprise ratio statistics of one or more datagroup averages. In some embodiments, the model update can detectdeviation from the ratio statistics of one or more data group averagesto determine future positive results. In some embodiments, the modelupdate features comprise one or more network or image graphics thatrepresent one or more models. In some embodiments, the model updatefeatures comprise one or more network or image graphics that representthe new model.

Once a local node has generated the model features 504, the local nodecan output the model update 505. In some embodiments, the local nodewill output the model update to a central module of the update system,which receives the model update 506. For example, the monitoring module211 of local node 210 can receive a model update from the diagnosismodule 212, and then the monitoring module 211 can send the model updateto the central module 201, which receives the model update.

Upon receiving a model update from a local node 506, a central module ofan update system will then consult a model database 507. In someembodiments, the central module consults a model database to determineif the model update is already present. In some embodiments, the centralmodule consults a model database to determine if the model updatereplaces an existing model in the database or is a novel model to thedatabase. In some embodiments, when the central module consults a modeldatabase and determines that the model update could replace or modify anexisting model in the database, the central module can pull modelinformation on the existing model. In some embodiments, modelinformation can include one or more of the following: model creationdate and time, date and time of when the model was last updated, and howmany local nodes currently use the model. For example, upon receivingthe model update from local node 210, the central module 201 checks themodel update against the available model database 202. The centralmodule 201 determines if the model update already exists in theavailable model database 202, and if it does, the central module 201will pull relevant information on any existing model.

After the central module of an update system consults a model database507, the central module will then determine the priority level of themodel update 508. In some embodiments, the priority level of the modelupdate will be listed as high, medium, or low. In some embodiments, thepriority level of the model update will be listed on a numerical scale,for example, between a range of 1 to 10 or other common numerical range.In some embodiments, the central module determines the priority level ofthe model update by comparing the model update features to apre-determined scale. In some embodiments, the central module determinesthe priority level of the model update by comparing the model updatefeatures to a model database. In some embodiments, the central moduledetermines the priority level of the model update by comparing the modelupdate features to model information stored in an existing modeldatabase. In some embodiments, the comparison of the model updatefeatures to the existing model information results in a priority grade,which is then turned into a priority level.

For example, after the central module 201 of update system 200 checksthe model update for credit card fraud detection against the availablemodel database 202, the central module 201 determines that a similarmodel already exists in the database and pulls information on theexisting model. The central module 201 then compares the model updatefeatures to the existing model information and calculates a prioritygrade. As a first example, the central module determines that theexisting model for credit card fraud detection has not been updated inover a year, that the model update is a direct replacement for theexisting model, and that the model update can increase performance ofdetecting credit card fraud over a range of use conditions. Thesedifferences result in a high priority grade, which the central module201 turns into a high priority level. As a second example, the centralmodule determines that the existing model for credit card frauddetection has been recently updated, and that the model update wouldonly be expected to increase performance of detecting credit card fraudwith a large enough user base that only few end users are known to have.These differences result in a relatively lower priority grade, which thecentral module 201 turns into a medium priority level.

After determining priority, the update system would then determine theapplicable end users for the new model 509. In some embodiments, thecentral module is determining which end users are applicable. In someembodiments, the central module determines which end users areapplicable for the model update by comparing the model update featureswith information on each end user, in addition to the priorityinformation of the new model. For example, if the model update forcredit card fraud detection has a medium priority, the central modulewill identify which local nodes in the update system are involved withcredit card fraud detection and then push out the model 510 to thoseidentified local nodes. The model update would not be pushed out to anyremaining local nodes, however, when each of those remaining local nodesinitiates an update process, for example, if enough time has gone bywithout an update to trigger the monitoring module, that local node maythen receive the update. In another example, if the model update forcredit card fraud detection has a high priority, the central module willoutput the model 510 to all local nodes. In another example, if themodel update for credit card fraud detection has a low priority, thecentral module will not push out the model to any local node right away,and instead wait for each local node to initiate an update process onits own.

Once the model update has been sent out from the central module, it isreceived by at least one local node 511. In some embodiments, the modelupdate is received by multiple local nodes simultaneously. In someembodiments, the model update is received by the monitoring module inany of the embodiments described herein.

In some embodiments, once a local node has received a model update 511,it is not installed automatically. First, the local node will consultthe current model database to see if the model update will replace anyexisting models 512. Then the local node will determine the relevance ofthe model update to the node 513. For example, in local node 210 ofupdate system 200 depicted in FIG. 2, the model update is received bythe monitoring module 211, and then passed along to the diagnosis module212. The diagnosis module 212 first consults the current model database215 and then determines the relevance of the model update to local node210. In some embodiments, the diagnosis module 212 will end the updateprocess after the determine relevance step 513. In some embodiments, thediagnosis module 212 will end the update process after the determinerelevance step 513 if the diagnosis module 212 determines that the modelupdate is not needed for the local node. In some embodiments, thediagnosis module 212 will end the update process after the determinerelevance step 513 if the diagnosis module 212 determines that the modelupdate is already present in the local node. In some embodiments, thediagnosis module 212 will automatically bypass the determine relevancestep 513 if the model update carries a high priority.

In some embodiments, once the local node has determined that the modelupdate would be relevant or necessary, the local node will determine ifit has permission to apply the model update 514. In some embodiments,the evaluation module of the local node determines if the local node haspermission to apply the model update. In some embodiments, a local nodewill not have permission to install the model update. In someembodiments, a local node will not have automatic permission to installany model update. In some embodiments, a local node must consult or askpermission from an end user prior to installing the model update 516.For example, once the diagnosis module 212 has either determined thatthe model update is relevant or that the model update has a high enoughpriority to bypass the determine relevance step 513, the model update ispassed along to the evaluation module 213. The evaluation module 213then checks the update permission settings of the local node. In someembodiments, if the evaluation module 213 determines that it does nothave permission to install the model update, the evaluation module 213will end the update process. In some embodiments, the evaluation module213 will consult an end user, for example, by issuing a user prompt orby sending an e-mail or other communication to the end user, beforeinstalling the model update.

The local node will install the model update once the local nodedetermines that it has permission to do so 515. In some embodiments, anevaluation module installs the model update. In some embodiments, anymodule of the update system installs the model update. In someembodiments, the model update installs one or more new models to acurrent model database in the local node. In some embodiments, the modelupdate replaces one or more models in a current model database in thelocal node. For example, after permission has been established, theevaluation module 213 updates the current model database 215 with themodel update.

In some embodiments, once the update 515 is complete, the local nodecreates an output report 517. In some embodiments, the output report isshared with an end user. In some embodiments, the output report isshared with a central module of an update system. In some embodiments,the output report contains information on the model update, including,for example, the type of model updated, whether or not any old modelswere replaced, the date and time of the update, whether the new model iscurrently active, or any combination thereof.

FIG. 6 depicts a flow chart of an exemplary method of pushing a modelupdate in an update system without a central module, where the modelupdate was created automatically from a local node in the update system600. In some embodiments, method 600 can be used with the update systemdepicted in FIG. 3. First, a local node in an update system will detecta change in the results from their existing models 601. In someembodiments, a local node in an update system will detect a change infraud detection rates. In some embodiments, the change in frauddetection rates is an increase in fraud detection greater than a pre-setthreshold. In some embodiments the change in fraud detection rates is asignificant increase or decrease in fraud over a given period of time.In some embodiments, a local node in an update system will detect achange in detected fraud magnitude. In some embodiments, the change infraud magnitude is an increase in the value or dollar amount of adetected fraud event greater than a pre-set threshold. In someembodiments, the change in fraud magnitude is a significant increase inthe value or dollar amount of a detected fraud event compared to arunning average or mean of detected events. For example, the diagnosismodule 312 as depicted in FIG. 3 analyzes system data 314 and detects anincrease in the fraud detection rate that is greater than a standarddeviation away from the 3-month running average fraud detection rate.

Once a change in the results from their existing models has beendetected 601, the local node will list all of the models involved inthat detection 602. In some embodiments, the local node will list all ofthe models directly involved with producing the events detected in step601. In some embodiments, the local node will list all of the modelsdirectly and indirectly involved with producing the events detected instep 601. In some embodiments, the local node will list all activelyrunning models when the events were detected in step 601. For example,the diagnosis module 312, with access to both the system data 314 andthe current model database 315, will list all of the algorithmic modelsthat were directly and indirectly involved with producing the fraudevents that were previously detected in step 601.

Once a local node has listed the models 602 relevant to the detectedchange 601, the local node will analyze the data involved in producingthe events that lead to the detected change 603. In some embodiments,the local node analyzes the system data to determine the features andconditions relevant to the models listed in step 602 in producing theevents that were detected in step 601. In some embodiments, the localnode analysis can include, but is not limited to, ordinary leastsquares, penalized regressions, generalized additive models, quantileregressions, logistical regressions, and gated linear models. In someembodiments, the local node analysis will be transformed variants of therelevant model or models that reduce the complexity of those models. Forexample, placing monotonicity constraints on a non-linear, non-monotonicmodel to orient the model around variable relationship known to be true,or the utilization of monotonic neural networks for machine learningapplications. In some embodiments, the relevant visualizations will berelated but less complex models that approximate the applicable model ormodels, especially machine learning models. For example, surrogatemodels, local interpretable model-agnostic explanations (LIME), maximumactivation analysis, linear regression, and sensitivity analysis.

Once a local node has listed the models 602 and analyzed the relevantdata 603, the local node can then generate the features of the modelupdate 604 that will be sent to the rest of the update system. In someembodiments, a diagnostic module of a local node generates the modelfeatures 604. In some embodiments, the features of the model update arelocal node agnostic, i.e., the model update is usable by any of thelocal nodes in the update system. Therefore, the model update generatedby the local node is stripped of any specific data of that local node.In some embodiments, the model update features comprise one or more ofthe following: one or more algorithms, creation date and time, number ofevents detected over given time period, metadata or high level aggregatestatistics such as total transactional value of time, and the thresholdpoint or points used to trigger the update. In some embodiments, themodel update features comprise ratio statistics of one or more datagroup averages. In some embodiments, the model update can detectdeviation from the ratio statistics of one or more data group averagesto determine future positive results. In some embodiments, the modelupdate features comprise one or more network or image graphics thatrepresent one or more models. In some embodiments, the model updatefeatures comprise one or more network or image graphics that representthe new model.

Once a local node has generated the model features 604, the local nodecan output the model update 605. In some embodiments, the local nodewill output the model update to at least one other local node of theupdate system, which receives the model update 611. In some embodiments,the local node will output the model update to all other local nodes ofthe update system. For example, the monitoring module 311 of local node310 can receive a model update from the diagnosis module 312, and thenthe monitoring module 311 can send the model update to the other localnode 320, which receives the model update.

In some embodiments, once a local node has received a model update 611,it is not installed automatically. First, the local node will consultthe current model database to see if the model update will replace anyexisting models 612. Then the local node will determine the relevance ofthe model update to the node 613. For example, in local node 310 ofupdate system 300 depicted in FIG. 3, the model update is received bythe monitoring module 311, and then passed along to the diagnosis module312. The diagnosis module 312 first consults the current models database315 and then determines the relevance of the model update to local node310. In some embodiments, the diagnosis module 312 will end the updateprocess after the determine relevance step 613. In some embodiments, thediagnosis module 312 will end the update process after the determinerelevance step 613 if the diagnosis module 312 determines that the modelupdate is not needed for the local node. In some embodiments, thediagnosis module 312 will end the update process after the determinerelevance step 613 if the diagnosis module 312 determines that the modelupdate is already present in the local node.

In some embodiments, once the local node has determined that the modelupdate would be relevant or necessary, the local node will determine ifit has permission to apply the model update 614. In some embodiments,the evaluation module of the local node determines if the local node haspermission to apply the model update. In some embodiments, a local nodewill not have permission to install the model update. In someembodiments, a local node will not have automatic permission to installany model update. In some embodiments, a local node must consult or askpermission from an end user prior to installing the model update 616.For example, once the diagnosis module 312 has determined that the modelupdate is relevant, the model update is passed along to the evaluationmodule 313. The evaluation module 313 then checks the update permissionsettings of the local node. In some embodiments, if the evaluationmodule 313 determines that it does not have permission to install themodel update, the evaluation module 313 will end the update process. Insome embodiments, the evaluation module 313 will consult an end user,for example, by issuing an user prompt or by sending an e-mail or othercommunication to the end user, before installing the model update.

The local node will install the model update once the local nodedetermines that it has permission to do so 615. In some embodiments, anevaluation module installs the model update. In some embodiments, anymodule of the update system installs the model update. In someembodiments, the model update installs one or more new models to acurrent model database in the local node. In some embodiments, the modelupdate replaces one or more models in a current model database in thelocal node. For example, after permission has been established, theevaluation model 313 updates the current model database 315 with themodel update.

In some embodiments, once the update 615 is complete, the local nodecreates an output report 617. In some embodiments, the output report isshared with an end user. In some embodiments, the output report isshared with a central module of an update system. In some embodiments,the output report contains information on the model update, including,for example, the type of model updated, whether or not any old modelswere replaced, the date and time of the update, whether the new model iscurrently active, or any combination thereof.

In some embodiments, the user of any of the systems disclosed herein canbe one or more human users, as known as “human-in-the-loop” systems. Insome embodiments, the user of any of the systems disclosed herein can bea computer system, artificial intelligence (“AI”), cognitive ornon-cognitive algorithms, and the like.

The following table is a non-exclusive and non-exhaustive list ofexamples using any of the systems and methods disclosed herein.

A credit card company uses multiple analytical models to evaluatetransactional credit fraud. The models detect abnormal variations infraud events, which are analyzed by the diagnosis module of the updatesystem. The diagnosis module triggers the monitoring module to check tosee if any model updates are available from a central module server, sothat the system has the most up-to-date models to analyze the abnormalvariations. An insurance company uses multiple analytical models toproduce an ensemble result used in investigations of insurance fraud. Inproducing their result, some of the models show a significant increasein a specific type of fraud detection under certain circumstances. Thediagnosis module detects this significant increase, and creates a modelupdate that includes the models that show the increase, the date andtime of the event detection, which type of fraud was detected, and underwhich general circumstances the fraud was detected. The diagnosis modulestrips out any specific company data from the model update, creating ageneral model that can be applied to any system data. The monitoringmodule then transmits this new model update to a central module server,where the update is analyzed and given a priority level, and thentransmitted in turn to other local nodes/end users in the update system.A bank employs a feedback mechanism that combines the analytical resultsfrom several models with human investigative results to determine that anew type of fraud is increasing in frequency. The feedback of humanresults is used by the diagnosis module to determine the specific modelfeatures that are used to detect this emergent fraud. The diagnosismodule communicates to the monitoring module the model features that areused to detect this new type of fraud and triggers feature generation sothat existing models can use these new features to perform betterdetection. An insurance company uses a variety of models to performevent detection analysis, the results of which are send to another modelthat looks at detection trends over time. This trend analysis discoversthat certain types of event detections increase in frequency duringcertain time frames, for example, during natural weather events. Thediagnosis module communicates this trend analysis result to themonitoring module, which is then transmitted the information to acentral server. The trend analysis update is then transmitted to certainother local nodes.

FIG. 7 depicts a block diagram of an example data processing system 700in which aspects of the illustrative embodiments are implemented. Dataprocessing system 700 is an example of a computer, such as a server orclient, in which computer usable code or instructions implementing theprocess for illustrative embodiments of any of the disclosures describedherein are located. In some embodiments, FIG. 7 represents a servercomputing device, such as a server, which implements the system forinterpreting analytical results described herein.

In the depicted example, data processing system 700 can employ a hubarchitecture including a north bridge and memory controller hub (NB/MCH)701 and south bridge and input/output (I/O) controller hub (SB/ICH) 702.Processing unit 703, main memory 704, and graphics processor 705 can beconnected to the NB/MCH 701. Graphics processor 705 can be connected tothe NB/MCH 701 through an accelerated graphics port (AGP). The networkadapter 706 connects to the SB/ICH 702. The audio adapter 707, keyboardand mouse adapter 708, modem 709, read only memory (ROM) 710, hard diskdrive (HDD) 711, optical drive (CD or DVD) 712, universal serial bus(USB) ports and other communication ports 713, and the PCI/PCIe devices714 can connect to the SB/ICH 702 through bus system 716. PCI/PCIedevices 714 may include Ethernet adapters, add-in cards, and PC cardsfor notebook computers. ROM 710 may be, for example, a flash basicinput/output system (BIOS). The HDD 711 and optical drive 712 can use anintegrated drive electronics (IDE) or serial advanced technologyattachment (SATA) interface. The super I/O (S/O) device 715 can beconnected to the SB/ICH 702.

An operating system can run on processing unit 703. The operating systemcan coordinate and provide control of various components within the dataprocessing system 700. As a client, the operating system can be acommercially available operating system. An object-oriented programmingsystem, such as the Java™ programming system, may run in conjunctionwith the operating system and provide calls to the operating system fromthe object-oriented programs or applications executing on the dataprocessing system 700. As a server, the data processing system 700 canbe an IBM® eServer™ System P® running the Advanced Interactive Executiveoperating system or the LINUX® operating system. The data processingsystem 700 can be a symmetric multiprocessor (SMP) system that caninclude a plurality of processors in the processing unit 703.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as the HDD 711, and are loaded into the main memory 704 forexecution by the processing unit 703. The processes for embodiments ofthe medical record error detection system can be performed by theprocessing unit 703 using computer usable program code, which can belocated in a memory such as, for example, main memory 704, ROM 710, orin one or more peripheral devices.

A bus system 716 can be comprised of one or more busses. The bus system716 can be implemented using any type of communication fabric orarchitecture that can provide for a transfer of data between differentcomponents or devices attached to the fabric or architecture. Acommunication unit such as the modem 709 or network adapter 706 caninclude one or more devices that can be used to transmit and receivedata.

Those of ordinary skill in the art will appreciate that the hardwarerequired to run any of the systems and methods described herein may varydepending on the implementation. Other internal hardware or peripheraldevices, such as flash memory, equivalent non-volatile memory, oroptical disk drives may be used in addition to or in place of thehardware depicted. Moreover, any of the systems described herein cantake the form of any of a number of different data processing systems,including but not limited to, client computing devices, server computingdevices, tablet computers, laptop computers, telephone or othercommunication devices, personal digital assistants, and the like.Essentially, any of the systems described herein can be any known orlater developed data processing system without architectural limitation.

The systems and methods of the figures are not exclusive. Other systems,and processes may be derived in accordance with the principles ofembodiments described herein to accomplish the same objectives. It is tobe understood that the embodiments and variations shown and describedherein are for illustration purposes only. Modifications to the currentdesign may be implemented by those skilled in the art, without departingfrom the scope of the embodiments. As described herein, the varioussystems, subsystems, agents, managers and processes can be implementedusing hardware components, software components, and/or combinationsthereof. No claim element herein is to be construed under the provisionsof 35 U.S.C. 112, sixth paragraph, unless the element is expresslyrecited using the phrase “means for.”

Although the present invention has been described with reference toexemplary embodiments, it is not limited thereto. Those skilled in theart will appreciate that numerous changes and modifications may be madeto the preferred embodiments of the invention and that such changes andmodifications may be made without departing from the true spirit of theinvention. It is therefore intended that the appended claims beconstrued to cover all such equivalent variations as fall within thetrue spirit and scope of the invention.

What is claimed is:
 1. A system for updating detection models,comprising: at least one local node comprising: a monitoring module; adiagnosis module; an evaluation module; one or more current detectionmodels; and system data produced by the current detection models; and amemory comprising instructions, which are executed by at least oneprocessor, configured to: detect, by the diagnosis module, a change inthe system data; determine, by the diagnosis module, a list of allcurrent detection models involved with the detection step; analyze, bythe diagnosis module, the system data involved with the detection step;generate, by the diagnosis module, a model update, wherein the generatedmodel update does not comprise any system data specific to a local nodethat generated the model update; receive, by the monitoring module, themodel update; determine, by the diagnosis module, the current detectionmodels; determine, by the evaluation module, if the model update shouldbe applied to the current detection models; determine, by the evaluationmodule, if the local node has permission to apply the model update;update, by the evaluation module, the current detection models with themodel update; and create, by the evaluation module, an output reportincluding a type of updated detection models, whether any old detectionmodels were replaced, a date and time of the model update, and whetherthe updated detection models are currently active.
 2. The system ofclaim 1, wherein the system further comprises at least two local nodes,wherein the system further comprises a central module, and wherein themonitoring module of each local node is in electronic communication withthe central module.
 3. The system of claim 2, wherein the system furthercomprises a database of all available models for the system inelectronic communication with the central module.
 4. The system of claim2, wherein the monitoring module is configured to receive model updates.5. The system of claim 4, wherein the monitoring module can receive amodel update from a system administrator or from a local node.
 6. Thesystem of claim 1, wherein the generated model update comprises one ormore algorithms, creation date and time, number of events detected overgiven time period, aggregate statistics, and the threshold point orpoints used to trigger the model update.
 7. The system of claim 1,further comprising: at least a second local node; a central module,wherein the monitoring module of each local node is in electroniccommunication with the central module; and a database of all availablemodels for the system in electronic communication with the centralmodule; and a memory comprising instructions, which are executed by atleast one processor, configured to: create a model update, comprising:detecting, by the diagnosis module, a change in system data;determining, by the diagnosis module, a list of all current detectionmodels involved with the detection step; analyzing, by the diagnosismodule, the system data involved with the detection step, by one or moreof ordinary least squares, penalized regressions, generalized additivemodels, quantile regressions, logistical regressions, and gated linearmodels; generating, by the diagnosis module, the model update; andtransmitting, by the monitoring module, the model update; distribute amodel update, comprising: receiving, by the central module, the modelupdate; analyzing, by the central module, the database of availablemodels; determining, by the central module, a priority level for themodel update; determining, by the central module, which local nodesshould receive the model update; and transmitting, by the centralmodule, the model update; and update at least one local node,comprising: receiving, by at least one monitoring module, a modelupdate; determining, by at least one diagnosis module, the currentdetection models; determining, by at least one evaluation module, if themodel update should be applied to the current detection models;determining, by the at least one evaluation module, if the local nodehas permission to apply the model update; and updating, by the at leastone evaluation module, the current detection models with the modelupdate.
 8. A computer implemented method in a data processing systemcomprising a processor and a memory comprising instructions which areexecuted by the processor to cause the processor to implement a systemupdating detection models, the method comprising: creating a modelupdate, comprising: detecting, by the diagnosis module, a change insystem data; determining, by the diagnosis module, a list of all currentdetection models involved with the detection step; analyzing, by thediagnosis module, the system data involved with the detection step, byone or more of ordinary least squares, penalized regressions,generalized additive models, quantile regressions, logisticalregressions, and gated linear models; and generating, by the diagnosismodule, the model update, wherein the generated model update does notcomprise any system data specific to a local node that generated themodel update; updating at least one local node, comprising: receiving,by a monitoring module of at least one local node, the model update;determining, by a diagnosis module of the local node, the currentdetection models in use by the local node; determining, by an evaluationmodule of the local node, if the model update should be applied to thecurrent detection models; determining, by the evaluation module of thelocal node, if the local node has permission to apply the model update;updating, by the evaluation module of the local node, the currentdetection models with the model update; and creating, by the evaluationmodule of the local node, an output report including a type of updateddetection models, whether any old detection models were replaced, a dateand time of the model update, and whether the updated detection modelsare currently active.
 9. The method of claim 8, wherein the methodfurther comprises updating at least two local nodes.
 10. The method ofclaim 9, wherein the method further comprises distributing, by a centralmodule, the model update.
 11. The method of claim 10, wherein the methodfurther comprises analyzing, by the central module, a database ofavailable models.
 12. The method of claim 10, wherein the method furthercomprises determining, by the central module, a priority level of themodel update.
 13. The method of claim 10, wherein the method furthercomprises determining, by the central module, which local nodes shouldreceive the model update.
 14. The method of claim 8, wherein thegenerated model update comprises one or more algorithms, creation dateand time, number of events detected over given time period, aggregatestatistics, and the threshold point or points used to trigger the modelupdate.
 15. The method of claim 8, further comprising: creating a modelupdate, comprising: detecting, by the diagnosis module, a change insystem data; determining, by the diagnosis module, a list of all currentdetection models involved with detecting the change; analyzing, by thediagnosis module, the system data involved with detecting the change, byone or more of ordinary least squares, penalized regressions,generalized additive models, quantile regressions, logisticalregressions, and gated linear models; generating, by the diagnosismodule, the model update; and transmitting, by the monitoring module,the model update; distributing the model update, comprising: receiving,by a central module, the model update; analyzing, by the central module,a database of available models; determining, by the central module, apriority level for the model update; determining, by the central module,which local nodes should receive the model update; and transmitting, bythe central module, the model update to at least one local node; andupdating at least one local node, comprising: receiving, by at least onemonitoring module, the model update; determining, by at least onediagnosis module, the current detection models; determining, by at leastone evaluation module, if the model update should be applied to thecurrent detection models; determining, by the at least one evaluationmodule, if the local node has permission to apply the model update; andupdating, by the at least one evaluation module, the current detectionmodels with the model update.
 16. The method of claim 8, wherein themodel update includes one or more network or image graphics representingthe updated detection models.